Data Science Certificate case studies: Lloyd Jansen Van Vuuren

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Lloyd Jansen Van Vuuren tells us how gaining the Certificate in Data Science has given him the confidence to become more involved in data science projects in his workplace.

I grew up in Zimbabwe and only came to the UK fifteen years ago, where I got a role as a trainee actuary. Currently, I work as a Financial Risk Actuary for a large life insurer that operates in the UK and Europe. Lately I have worked within Internal Audit, before moving into a Risk function.  

I enrolled on the programme because I wanted to gain an introduction to data analytics and to have a working knowledge of the key concepts and themes currently developing in the data science industry. I had often heard these concepts mentioned, but not in any depth. Every person I knew held only a fragment of understanding and it never seemed to go anywhere.

A few years ago, I became involved in helping to craft a data analytics strategy document for one of the teams I worked in, and while I hoped it was a helpful contribution, I felt my knowledge was still lacking. Additionally, with the way things are going globally with a move to increasingly digital offerings, and the greater reliance on data to perform any form of assurance and oversight work, I felt I needed to get my hands dirty and begin to develop and grow my understanding.

I don’t think I will ever become a professional data analyst, but I would like to be able to hold a decent conversation and get involved in work discussions around how data analytics is becoming embedded in situations, and how best to use and leverage the capabilities that become available.

Before the course, I had a very basic understanding but no hands-on experience. I had done a lot of spreadsheet manipulation in my younger years, alongside manipulating data using databases and performing automated checks to understand the data and how it would be used. But it was only ever limited, sporadic and to get the data in a form that I could then use to produce a report or summary. There was always someone else more data savvy, so I never had to learn more than the basics to get along.

What I liked about the programme was that it was a good introduction that allowed people of varying abilities to get a foothold in data science, with the option to go into more detail if needed. Being surrounded by a helpful community of students and tutors assisted the journey and allowed debate and conversation to follow where the areas of joint interest lay. I discovered that there were several colleagues on the same course, all working in a variety of roles within the wider actuarial function.

The course allowed a flexible approach to reading and getting through the material before completing the assignments. Because some of the assignments required a potential application to a theoretical or real work situation, it made the course exceptionally practical and helped me to ‘earth’ it and take a lot of what I learnt into the office immediately.

There was a lot of material to get through, but thankfully there were sections that were optional which could be perused after the course, as the content is still available for a limited time to the students. There were a variety of contributors, which mixed up the tone and style across the course.

I used my own time after work and over lunch breaks to go through the material and complete the assignments. It was not too time intensive but did require focused effort. I have a young family and work full time, but it was a manageable commitment I was able to see through. Ideally, I would have liked to have spent more time on the course before I had to submit assignments, but in a sense the reality of having to weave it alongside life makes the course accessible to anyone at any stage in life who desires to get a better understanding of data, data analytics, data science, data visualisation and all the related shades of the wider topic.

By the end of the course, I had gained an understanding of the key terms, phrases, techniques and thinking in data science, some practical applications for the workplace and some case studies to think through alternative approaches. Because I have access to the material, I can still dip in and refresh when I need to or when a work situation arises that aligns to what I had learnt. Additionally, I gained a certificate from a reputable organisation that indicates that I am on a trajectory along the data science learning curve.

My workplace is currently increasing their data analytics capability and I intend to be in line to encourage and grow this capability. There are several initiatives across the company where the focus is on creating data efficiencies and using data in ways that create insight and provide better analysis that I could see myself being able to add value to, instead of staying on the edge unable to contribute any insight, challenge or proposals. The course has allowed me to get semi-fluent in the terminology used and the possible uses of data that I can now see happening all around me.

I have helped to host several data analytics industry sessions for data professionals and intend to try and be proactive in stimulating discussion and positive change through the use of data and its analysis, whatever role I find myself in.

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